Now that you know how to create graphics and visualizations in R, you are armed with powerful tools for scientific computing and analysis. With this power also comes great responsibility. Effective visualizations is an incredibly important aspect of scientific research and communication. There have been several books (see references) written about these principles. In class today we will be going through several case-studies trying to develop some expertise into making effective visualizations.
The worksheet questions for today are embedded into the class notes.
You can download this Rmd file here
Note, there will be very little coding in-class today, but I’ve given you plenty of exercises in the form of a supplemental worksheet (linked at the bottom of this page) to practice with after class is over.
Fundamentals of Data Visualization by Claus Wilke.
Visualization Analysis and Design by Tamara Munzner.
STAT545.com - Effective Graphics by Jenny Bryan.
ggplot2 book by Hadley Wickam.
Callingbull.org by Carl T. Bergstrom and Jevin West.
Write some notes here about what “effective visualizations” means to you. Think of elements of good graphics and plots that you have seen - what makes them good or bad? Write 3-5 points.
Question: Evaluate the strength of the claim based on the data: “German workers are more motivated and work more hours than workers in other EU nations.”
Very strong, strong, weak, very weak, do not know
Weak. The bar plot is misleading in its scale of the x-axis. The country with the least hours worked is France which is only 3 hours per week less than Germany. The entire range of weekly hours only spans 3.8 hours. Also, since it only shows the average, we do not know how skewed the distribution of weekly hours works is or how many workers contributed to the average. If we knew the proportion of the population working versus not working, perhaps we could deduce more.
Main takeaway: Bar plots should start at zero, the grid lines are distracting, be sure to use space wisely.
Question: For the years this temperature data is displayed, is there an appreciable increase in temperature?
Yes, No, Do not know
Do not know, because we do not even see the year labels for the x-axis. The range of the y-axis seems too large for the data to see any trend.
Main takeaway: Properly label plot (axes, title, etc.), there is approximately a two-degree increase that is obscured by the way the data is presented so that it is up to the researcher to choose appropriate scales for axes values.
Question: Evaluate the strength of the claim based on the data: “Soon after this legislation was passed, gun deaths sharply declined.”
Very strong, strong, weak, very week, do not know
The evidence is strong, but correlation does not equal causation.
Main takeaway: I didn’t even realize the y-axis numbers increasing from bottom to top, so my above statement is clearly incorrect.
Great resource for selecting the right plot: https://www.data-to-viz.com/ ; encourage you all to consult it when choosing to visualize data.
We will be filling these principles in together as a class
Instructions: Here is a code chunk that shows an effective visualization. First, copy this code chunk into a new cell. Then, modify it to purposely make this chart “bad” by breaking the principles of effective visualization above. Your final chart still needs to run/compile and it should still produce a plot.
How many of the principles did you manage to break?
Did you know that you can make interactive graphs and plots in R using the plotly library? We will show you a demo of what plotly is and why it’s useful, and then you can try converting a static ggplot graph into an interactive plotly graph.
This is a preview of what we’ll be doing in STAT 547 - making dynamic and interactive dashboards using R!
#install.packages('plotly')
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(gapminder))
suppressPackageStartupMessages(library(plotly))
p <- ggplot(gapminder, aes(x=gdpPercap, y=lifeExp,
color=continent)) +
geom_point()
p
#make interactive
p %>%
ggplotly()
#click on legend once to make those points disappear; click twice to isolate only those points
# syntax for plotly
gapminder %>%
plot_ly(x=~gdpPercap,
y=~lifeExp,
color=~continent,
type='scatter',
mode='markers' #markers for points,
)
You are highly encouraged to the cm013 supplemental exercises worksheet. It is a great guide that will take you through Scales, Colours, and Themes in ggplot. There is also a short guided activity showing you how to make a ggplot interactive using plotly.